Image sensors have become ubiquitous. They are widely used in digital still cameras, cellular phones, security cameras, as well as, medical, automobile, and other applications. The technology used to manufacture image sensors, has continued to advance at great pace.
One feature that is useful in connection with image sensors is feature detection. For example, some devices that include image sensors are capable of capturing an image in response to detecting a given feature in an image. For example, an image may be captured in response to detecting that a person in the image frame is smiling. Conventionally, classifiers of features from training images requiring significant storage space have been loaded into memory to be used to compare with a current image that an image sensor is currently imaging. To accommodate the variety in size, shape, and shades that features (e.g. mouths and teeth) include, a large number of classifiers of features from training images may be required to sufficiently identify a smile, for example. Furthermore, additional training images are necessary to identify additional features (e.g. eyes for blink detection). Therefore, feature detection takes significant memory resources.
In addition to memory resources, the conventional feature detection also requires significant processing resources to compare the current image to the variety of classifiers of features from training images. This may cause time delays in capturing the desired images and drain battery resources. Hence, a feature detection device and/or method that would reduce memory, processing, and/or power consumption would be desirable.